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Salp swarm algorithm (SSA) is an excellent meta‐heuristic algorithm, which has been widely used in the engineering field. However, there is still room for improvement in terms of convergence rate and solution accuracy. Therefore, this paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA). The parallel trick is to split the initial population uniformly into several subgroups and then exchange information among the subgroups after a fixed number of iterations, which speeds up the convergence. Communication strategies are an important component of parallelism techniques. The Taguchi method is widely used in the industry for optimizing product and process conditions. In this paper, the Taguchi method is adopted into the parallelization technique as a novel communication strategy, which improves the robustness and accuracy of the solution. The proposed algorithm was also tested under the CEC2013 test suite. Experimental results show that PTSSA is more competitive than some common algorithms. In addition, PTSSA is applied to optimize the operation of a heatless combined cooling‐power system. Simulation results show that the optimized operation provided by PTSSA is more stable and efficient in terms of operating cost reduction. [ABSTRACT FROM AUTHOR]

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP‐complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing high‐strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high‐strength CAG problem. The sub‐optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high‐covering strength test suites. The QSSMA method could successfully build high‐strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

The ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance. [ABSTRACT FROM AUTHOR]

Inertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi‐peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Lévy flight strategy with DCWQPSO algorithm. Lévy flight is a non‐Gaussian random process. The algorithm introduces Lévy flight strategy to change the mutation formula of particles and uses the characteristics of Lévy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast‐tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (a) In order to improve the optimization performance and reduce the computation time, we propose a multidomain virtual network embedding algorithm (MP‐VNE) adopting the centralized hierarchical multidomain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (b) In order to simplify the multiobjective optimization problem, we transform the multiobjective problem into a single‐objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (c) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP‐VNE algorithm has better performance than MC‐VNM, LID‐VNE, and other algorithms in terms of delay, cost and comprehensive indicators. [ABSTRACT FROM AUTHOR]

In this paper, a new frequency domain behavior model for radio frequency (RF) power transistors based on a hyper‐parameter optimized Gaussian process regression (GPR) method is presented. The chaos game optimization (CGO) algorithm is used to optimize GPR hyperparameters, resulting in the CGO‐GPR model. The basic theory as well as the details of the modeling process are presented. Validation of the model is conducted using a 10‐watt GaN power transistor. Compared to the standard GPR model, the proposed model achieved a significant improvement. Furthermore, the comparison with the particle swarm optimization (PSO) based GPR model (PSO‐GPR) showed that the proposed model allows achieving superior performance, thereby confirming the effectiveness of the developed modeling technique. [ABSTRACT FROM AUTHOR]

Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation based method to diagnose winding deformation and turn‐to‐turn fault of power transformers. First, an estimation model of transformer parameters is built, in which five equations are taken in account including voltage loop equation, active loss equation, input impedance equation, no‐load current equation and no‐load loss equation. Then, particle swarm optimization (PSO) is used to solve the model, and the characteristics of estimation data under winding faults are analyzed. At last, based on the estimation data, random forest (RF) algorithm is employed to classify transformer states and realize fault diagnostic. The simulation results show that the proposed parameter estimation method has high precision and are not affected by the factors including load power factors and the type and degree of winding deformation, and the impact of load rates can be avoided; the fault diagnostic scheme based on RF is quite sensitive and effective. The proposed method eliminates the need of transformer outage, and compared with other methods based on parameter estimation, it can distinguish winding deformation and turn‐to‐turn fault. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

When traditional response surface method is used to evaluate the reliability of concrete‐filled steel tubular arch bridge, due to its complex structure and highly nonlinear implicit function, the response surface fitting accuracy is not high, and the reliability accuracy is difficult to meet the requirements of design specifications. In order to solve the above problems, this paper chooses dynamic Bayesian networks (DBN) which is suitable for solving the problem of multiple state unit or system uncertainty to build implicit function of the response surface function. And this paper combines DBN and particle swarm optimization algorithm based on simulated annealing algorithm (PSOSA) to improve efficiency of model parameter optimization. It can make the construction of implicit function simulate the real structure of the limit state function. Then this paper verifies the suitability for this kind of complex structure reliability assessment of DBN‐PSOSA hybrid algorithm. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of a concrete filled steel tube arch bridge project is evaluated. The results show that this method improves the calculation accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Particle swarm optimization is widely applied in self‐potential inversion, but most of these applications are limited to simple polarized bodies like inclined sheets and spheres. In this paper, two variants of particle swarm optimization are formed by introducing the resistivity constraint matrix and the damping factor and are then applied to two‐dimensional self‐potential source inversion. The two variants are combined to balance the exploration and exploitation capabilities in different situations, and the rationality of the parameter selection is validated through the stability analysis. To ensure both compactness and smoothness of the inversion results, we adopt an objective function based on L1–L2 norm regularization. Finally, we verify the effectiveness of the proposed algorithm through synthetic examples, the Cu–Fe model sandbox experiment and a field example. By imposing certain constraints, particle swarm optimization also shows great potential in high‐dimensional self‐potential source inversion, instead of being limited to the inversion of a few parameters. [ABSTRACT FROM AUTHOR]

This paper presents a new modified variable step size Fractional Order Incremental Conductance (FOIC) with maximum power point tracking using Fractional Order PID controller tuned by bio‐inspired Particle Swarm Optimization (PSO) to find optimal gain values of fractional integrator order (λ) and fractional derivative order (μ). The classical incremental conductance and FOIC show drawbacks under changing irradiance, oscillation around maximum power point (MPP) which decreases its convergence speed. To resolve these prone a variable step size FOIC is proposed to achieve an adaptive duty cycle via tuning of FOPID through PSO. The robustness of the proposed technique is judged by its steady‐state and dynamic response with fast converges, less response time, overshoot, and ripples under changing environmental conditions. Furthermore, the performance of the proposed technique is evaluated by comparing it with a fixed step size conventional incremental conductance algorithm and FOIC. [ABSTRACT FROM AUTHOR]

Strip crown prediction model based on support vector machines (SVMs) is proposed, and the improved adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize the model parameters ( C , σ ) in this paper. A set of online inspection data from the steel plant is adopted to train and test model. The overall performance of the model is evaluated by the decision coefficient ( R 2), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Comparing the results calculated by other relevant models and the present model with same experimental data, the accuracy of the model presented in this paper is verified. The AMPSO‐SVR model can be successfully applied to the prediction of strip crown in hot rolling. Both prediction accuracy and generalization ability of the new model have achieved good results. The proposed model provides a new method and idea for shape control and optimization research in hot strip rolling process. [ABSTRACT FROM AUTHOR]

Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed. [ABSTRACT FROM AUTHOR]

The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross‐over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively. [ABSTRACT FROM AUTHOR]

Summary: The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure. [ABSTRACT FROM AUTHOR]

Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely. Methods: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time. Results: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%. Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness. Conclusions: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS. [ABSTRACT FROM AUTHOR]

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature‐inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed‐effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often‐used technique to evaluate high dimensional integrals, to search for D‐efficient designs for estimating parameters in nonlinear mixed‐effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature‐inspired metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics. [ABSTRACT FROM AUTHOR]

Summary: A novel multiband pinwheel fractal antenna with monopole configuration is proposed. The pinwheel fractal antenna has been fabricated on a commercially available FR4 substrate with a thickness of 1.6 mm. This paper proposes a novel pinwheel antenna with a 50% reduction in the dimension of width with a reduced level of return loss. The proposed antenna has multiband characteristics covering the bands of GSM (900 MHz), DCS (1710 GHz), PCS (1900 MHz), UMTS (1920 MHz), and WLAN (2.4–5.9 GHz) applications. The projected antenna resonates at multiple frequencies with acceptable antenna gain and directivity. The proposed antenna design is optimized by particle swarm optimization (PSO) algorithm. The parametric studies and the design rule for the proposed fractal have been reported. The prototypes of the proposed multiband antenna have been demonstrated. The measured and simulated results ensure the multi‐resonance characteristics and authorize the projected design method and gratify the necessities of handheld wireless communication systems. The performance analysis proves that the proposed novel pinwheel antenna provides reduced return loss. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed. [ABSTRACT FROM AUTHOR]

Summary: Energy‐efficient clustering and routing are two well‐known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP‐hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi‐objective particle swarm optimization with Levy distribution (MOPSO‐L) algorithm. Since the parameters in WSN are related to one another, multi‐objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO‐L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO‐L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) with fluctuating environs might be susceptible to diverse types of malicious cyber‐attacks, and they are mostly dependent on the authentication and encryption algorithm to astound this challenge. Most predominant routing schemes in literature are fall backs in characterizing the malicious nodes on networks due to the real time variation of routing information. Therefore, a reliable and trustworthy inter‐correlated routing scheme based on Block chain, Meta‐heuristic, and Deep Learning Algorithms are presented in this paper. The disseminated routing info in the WSN is handled by Block chain strategy, in which the optimal routing is accomplished with the help of Salp Swarm Optimization algorithm. The routing info variations between the nodes are envisaged and the optimal routing decisions are done by using the Deep Convolutional Neural network algorithm. The proposed routing scheme is implemented in NS2 and its performance is evaluated based on latency, energy consumption, and throughput metrics are analyzed. The efficiency of the method is improved as 97% and the evaluation is done for the malicious attacks, latency, and the delay. The comparison is made for the existing methods as particle swarm optimization, Markov decision process, security disjoint routing‐based verified message, trusted‐cluster–based routing, and reinforcement learning‐based neural network (RLNN) with the proposed method for the delay ratio. [ABSTRACT FROM AUTHOR]

An adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional-integral-derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller. [ABSTRACT FROM AUTHOR]

Nowadays, more and more wind turbine generators ( WTGs) are being integrated into power systems. In this paper, an optimal WTG allocation model with the objective of minimizing the annual cost is proposed. The model can take into account the time-sequential correlation between the wind speed and load demand, as well as four kinds of active management measures, i.e. regulating the on-load tap changer of the transformer, curtailing the output power of WTG, regulating the power factor of WTG, and managing the demand side. The time-sequential correlation is handled by the joint probability distribution method. A hybrid solving strategy combining the fuzzy adaptive particle swarm optimization algorithm and primal-dual interior point method is developed to get the optimal solutions. Case studies carried out on the IEEE 33-bus active distribution network verify the feasibility of the developed model and the high efficiency of the proposed solving strategy. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a hybrid bio‐inspired algorithm is proposed to enhance the performance of channel estimation of the orthogonal frequency‐division multiplexing–interleave division multiple access (OFDM–IDMA) system. This algorithm is a fusion of the lion algorithm and group search optimization algorithm named as SU‐LA, scrounger updated lion algorithm. It works on the optimal selection of pilot tone positions that ensure the attainment of the least value of mean square error. Polar code is used for error detection and correction along with Quadrature Amplitude Modulation (QAM) and 16 QAM techniques. Carrier frequency offset (CFO), user, and Eb/No factors are selected to analyze their impact on the BER performance in the presence of Rayleigh channel. It is observed that the proposed SU‐LA‐based system with the QAM technique achieves 91.23% enhancement in BER at zero CFO as compared to harmony search‐based system for Eb/No of 12 dB. Further, in the presence of 0.1 CFO, the proposed system retains BER performance. The experimentation is also carried with 16 QAM over various CFOs and the number of users. The BER performance of the proposed system is also superior to the particle swarm optimization (PSO) and genetic algorithm (GA)‐based system. The effect of peak‐to‐average power ratio (PAPR) and symbol time offsets is not considered. [ABSTRACT FROM AUTHOR]

Summary: The high peak‐to‐average power ratio (PAPR) is the main challenge of orthogonal frequency division multiplexing (OFDM) systems. Partial transmit sequence (PTS) is a useful approach to diminish the PAPR. Although the PTS approach significantly decreases the PAPR, it requires to explore all possible sequences of phase weighting factors. Hence, the computational cost exponentially increases with the number of divided subblocks. This paper proposes a novel PTS technique based on ant colony optimization (ACO) to diminish the high PAPR and computational cost of OFDM systems. By the new representation of phase factors as a graph, the improved ACO algorithm is combined with the PTS method to explore the optimal compound of the phase rotation factors. Simulation results represent that the proposed ACO‐based PTS approach significantly reduces the PAPR and improves the computational cost at the same time. A comparative analysis of the other meta‐heuristics shows that the ACO‐PTS approach outperforms the genetic algorithm, particle swarm optimization, and gray wolf optimization in terms of reducing PAPR. [ABSTRACT FROM AUTHOR]

Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels. [ABSTRACT FROM AUTHOR]

Summary: Wireless video sensor network (WVSN) has attracted much research interest due to its multi‐faceted potential applications in different fields. An energy‐efficient protocol is fundamental for WVSN since the network has to deal with traffic‐intensive multimedia data, such as images and videos. A few issues come in front for any such setting like energy inefficiency, throughput unfairness, and low network capacity. In many cases, we need to strike a balance between required parameters or aspects, but there is no alternative to ensuring appropriate energy efficiency in the process. Keeping this in mind, this paper proposes a holistic solution named energy‐efficient bio‐inspired Mimosa pudica (EEBIMP) algorithm. EEBIMP for WVSN can reduce the time delay and avoid unnecessary energy consumption due to clustering the network. Our proposed algorithm has been compared with one of the most recent notable solutions, like GREENNESS (Green wiReless vidEo sENsor NEtworks uSing out‐of‐band Signalling), PSO (Particle Swarm Optimization), and CUCKOO search algorithm. The result shows that EEBIMP achieved 95% of energy efficiency and throughput when compared with other algorithms of GREENNESS, PSO, and CUCKOO. [ABSTRACT FROM AUTHOR]

This paper proposes a servo‐tuning system for five‐axis machine tools using a virtual feed drive system. The feed drive systems were first modeled to include the influence of friction and backlash. The particle swarm optimization (PSO) algorithm was adopted to identify and estimate the optimal system parameters. Therefore, the virtual system can be used to comprehend the influence of servo parameters on machining performance. The proposed approach was verified using the QC20‐W wireless ballbar to measure the results of the circular test. The experimental results indicate that our proposed approach can improve the rounding error by 21% and mismatch between the feed axes by 16%. In addition, based on parameter variations, such as variations in bandwidth, resonance frequency, and contour error, we propose methods to diagnose online contouring errors. [ABSTRACT FROM AUTHOR]

Summary: With the rapid proliferation of information and communication technology (ICT), the vast amount of available data creates information overload. The Websites and e‐commerce applications employ several information filtering methods such as personalized recommender system to manage the information overload. The recommender system assists the users in obtaining the desired list of products based on their interest. Several existing research works focus on the novelty or unexpectedness in the recommendation list while ensuring the quality to enhance the recommendation mechanism. It is essential to balance the unexpected and useful products or services to generate the satisfactory personalized recommendations with novelty. Thus, this paper proposes a novelty‐driven movie suggestion using integrated matrix factorization and temporal‐aware clustering optimization (NOMINATE). The proposed approach determines the personalized preferences through probabilistic matrix factorization (PMF) and contextually updates the rules and extracts the user preferences based on the inherent features of both the users and movies with temporal information. The NOMINATE approach also suggests the novelty‐driven, and desired top‐N movies to the users through the K‐means, and particle swarm optimization (PSO)‐based clustering algorithm with the help of LOD source. To identify the expert users, the NOMINATE approach applies the K‐means and PSO‐based clustering algorithm to enrich the personalized features of the users. Moreover, it integrates the relevant features with the preferred set of features for each user using the LOD source and decides a set of optimal preferences of the users. Finally, the NOMINATE approach generates the top‐N recommendation list for the corresponding user through ranking method. The experiment results stipulate that the NOMINATE approach personalize the top‐N movie recommendations with high performance regarding accuracy and novelty when compared with the existing recommendation method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the quantum‐behaved developed swarm optimizer is proposed for optimal design of real‐size building structures in which the quantum computing is introduced into the standard developed swarm optimizer. In this method, the position‐updating process for the search agents is conducted by simultaneous utilization of the so far best position of all particles, center of mass of all particles, so far best position of each particle, and the mean best position of all particles in which the first two of these aspects satisfy the exploration phase of the algorithm, whereas the other two are utilized for improving the exploitation phase of the proposed method. In order to evaluate the capability of the proposed method in dealing with difficult optimization problems, three real‐size building structures are considered, namely, a 10‐story building with 1,026 structural members, a 20‐story building with 3,860 members, and a 60‐story building with 8,272 members. The overall performance of the proposed quantum‐behaved developed swarm optimizer is compared with that of the standard developed swarm optimizer and other approaches. The obtained results proved that the proposed method is capable of providing better results for the considered examples than are the other algorithms. [ABSTRACT FROM AUTHOR]

Summary: Computational intelligence techniques have widespread applications in the field of engineering process optimization, which typically comprises of multiple conflicting objectives. An efficient hybrid algorithm for solving multi‐objective optimization, based on particle swarm optimization (PSO) and artificial bee colony optimization (ABCO) has been proposed in this paper. The novelty of this algorithm lies in allocating random initial solutions to the scout bees in the ABCO phase which are subsequently optimized in the PSO phase with respect to the velocity vector. The last phase involves loyalty decision‐making for the uncommitted bees based on the waggle dance phase of ABCO. This procedure continues for multiple generations yielding optimum results. The algorithm is applied to a real life problem of intercity route optimization comprising of conflicting objectives like minimization of travel cost, maximization of the number of tourist spots visited and minimization of the deviation from desired tour duration. Solutions have been obtained using both pareto optimality and the classical weighted sum technique. The proposed algorithm, when compared analytically and graphically with the existing ABCO algorithm, has displayed consistently better performance for fitness values as well as for standard benchmark functions and performance metrics for convergence and coverage. [ABSTRACT FROM AUTHOR]

Most often clinicians require automated computer‐aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly‐based features are used to improve the accuracy of well‐known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross‐sectional MRI of 65 non‐dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above‐mentioned three individual swarm intelligence algorithms is compared with non‐swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above‐mentioned swarm intelligence algorithms while using dragonfly‐based features and Fuzzy C means‐based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly‐based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly‐particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above‐mentioned individual classifiers yield accuracy of less than 66%. [ABSTRACT FROM AUTHOR]

Due to time‐varying external disturbances and uncertain system models, distributed cooperative controllers with poor adaptability are unable to meet the cooperative control requirements of multiple permanent magnetic maglev trains in virtual coupling mode. In this work, a new effective distributed auto disturbance rejection resilient controller based on the optimized deep deterministic policy gradient algorithm (DDPG) is proposed. The DDPG algorithm is used to improve the adaptability of the controller against the time‐varying disturbances. An adaptive particle swarm optimization method (APSO) is also proposed to optimize the hyperparameters of DDPG in the search space. The simulation results show that, compared to the particle swarm optimization (PSO)‐actor‐critic (AC), PSO‐policy gradient (PG), and PSO‐DDPG algorithms, the proposed APSO‐DDPG algorithm performs better during training and verification. The proposed method achieves adaptive online adjustment of the controller parameters effectively and greatly improves the stability of cooperative control. [ABSTRACT FROM AUTHOR]

This paper proposes a recurrent neural fuzzy controller (RNFC) approach based on a self-organizing improved particle swarm optimization (SOIPSO) algorithm used for solving control problems. The proposed SOIPSO algorithm can adaptively determine the number of fuzzy rules and automatically adjust the parameters in an RNFC. The proposed learning algorithm consisted of phases of structure and parameter learning. Structure learning adopts several subswarms to constitute the adjustable variables in fuzzy systems, and an elite-based structure strategy determines the suitable number of fuzzy rules. This paper proposes an improved particle swarm optimization technique, which consists of the modified evolutionary direction operator (MEDO) and traditional PSO techniques. The proposed MEDO method used the EDO and migration operation to improve the search ability of a global solution. Finally, the proposed RNFC approach based on the SOIPSO learning algorithm (RNFC-SOIPSO) was adopted to control a magnetic levitation system. Experimental results demonstrated that the proposed RNFC-SOIPSO model outperforms other models. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The buffer allocation problem is an important issue in production lines design. In this paper, we present new evaluation and optimization methods to optimally allocate buffers in unreliable production lines. Through analyzing different states of the machines and buffers by Markov process and incorporating the aggregation method, we make an evaluation on the system availability, instead of the throughput rate of the line. The optimization method is proposed by combining particle swarm optimization and estimation of distribution algorithm to maximize the system availability. It generates the new populations by estimation of distribution algorithm and particle swarm optimization to take their respective advantages in global and local optimization. Numerical tests and simulations are performed to validate the performance of the evaluation and optimization methods. The results indicate the effectiveness and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Accurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution. [ABSTRACT FROM AUTHOR]

Summary: In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded. [ABSTRACT FROM AUTHOR]

Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness-based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth-order Butterworth low-pass analog active filter and second-order state variable low-pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low-pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The virtual resource allocation in cloud computing is becoming a critical issue. In order to meet the task requirements of different users, virtual machines need to be placed on physical machines through virtualization technology in the data center. However, in this process, the total load balance, energy consumption, and resource utilization of physical machines should be considered. Therefore, two models are established for two different optimization targets, respectively. The first model is built to minimize the degree of load imbalance. The second model is built to maximize the resource utilization and minimize the energy consumption. To gain better results of virtual machines placement, we propose a new algorithm called resampled binary particle swarm optimization (RBPSO). To enhance the global search ability of BPSO, we add the re‐sampling, mutation and small vibration process to it, named RBPSO, for the purpose of maintaining the diversity of the population, reducing redundant calculation and thereby improving the ability and efficiency of the algorithm. Then, the RBPSO is used to solve the deployment problem of virtual machines in cloud computing. The experiments show that the proposed model is reasonable and RBPSO performs better than BPSO and genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

Here, the multi-objective optimisation algorithm speed-constrained multi-objective particle swarm optimisation is applied to the synthesis of uniformly excited sparse-rectangular planar array with low sidelobe level. Numerical simulations have been conducted to test the performance of multi-objective optimisation algorithm when used in this synthesis problem, and resulting radiation patterns are compared with those from other existing synthesis algorithms in literatures. Comparative results illustrate that the proposed algorithm has obtained the lowest maximum sidelobe level (MSLL) in φ = 0 plane (φ is the elevation angle) of radiation pattern combined with the lowest maximum MSLL in φ = π/2 plane. In addition, convergence performance of the multi-objective optimisation algorithm when used in this synthesis problem has also been investigated here. [ABSTRACT FROM AUTHOR]

As a new algorithm to achieve sparse aperture inverse synthetic aperture radar (SA‐ISAR) imaging, the alternating direction method of multipliers (ADMM) suffers from selection of model parameters and the inability to discriminate the target and non‐target components. Therefore, an improved ADMM (I‐ADMM) algorithm is proposed to solve these problems. First, the particle swarm optimisation (PSO) algorithm is utilised to obtain the optimal model parameters. Aiming to adequately preserve the target's shape information in the ISAR image, a modified image entropy (IE) is proposed to be the objective function of the PSO. Then, a new regularisation function is obtained from the ADMM based ISAR image. The proposed algorithm can enhance the target components and suppress the non‐target components by utilising the regularisation function. Finally, results based on the simulated and measured data show that the proposed I‐ADMM algorithm not only achieves at least 9.4% improvement in IE but also greatly improves the image quality as visually compared with the traditional ADMM algorithm which validates the effectiveness and superiority of the proposed algorithm. [Correction added on 13/09/23, after first online publication: In the following sentence in abstract "Finally, results based on... the proposed algorithm." 10% was changed as 9.4%.] [ABSTRACT FROM AUTHOR]

Summary: Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta‐heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non‐dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta‐heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. [ABSTRACT FROM AUTHOR]

Optimization and design of mobile wireless sensor networks (MWSNs) must assure adequate spatial coverage of the site. The spatial coverage optimization aims to enrich discoverability of MWSN by specifying mobile sensors geographical locations in order to maximize their coverage. In this paper, an enhanced metaheuristic algorithm called "firefly algorithm with crossover and detection phases" is introduced for optimizing the area coverage percentage of MWSN. The proposed algorithm is tested on many datasets with different criterions and compared with other algorithms including differential evolution, whale optimization algorithm, and flower pollination algorithm. The experimental results are analysed with one‐way ANOVA test. In addition, the proposed algorithm is compared with particle swarm optimization, and the results are analysed with Wilcoxon signed‐rank test. The overall analysis results prove the prosperity and efficient exploration of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (SR) requirements for wind power incorporation are determined according to the obtained PDF and CDF, and by converting the wind power into a chance constrained form, a model for dynamic economic emission dispatch with wind power is constructed. A hybrid multi-objective algorithm that integrates differential evolution (DE) and particle swarm optimisation (PSO) algorithm is put forward to solve the proposed model. The algorithm is implemented based on the Pareto dominance theory and a dynamic external archive set, and fully exploits the advantages of DE and PSO. An improved calculation method of crowding distance and Pareto solution set reduction rule are also employed to enhance the performance of the proposed algorithm. Also, three performance indicators are introduced to evaluate the performance of the algorithm. Two distinct test systems are performed to verify the proposed model and algorithm, and the results show that they are effective and reasonable. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP‐hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane‐inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane‐inspired approach to introduce a conventional membrane‐inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane‐inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO. [ABSTRACT FROM AUTHOR]

Summary: One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi‐objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta‐heuristic algorithms: Moth‐Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi‐Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network. [ABSTRACT FROM AUTHOR]

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade‐off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA‐GWO‐PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA‐GWO‐PSO approach with some conventional meta‐heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state‐of‐ art literature. [ABSTRACT FROM AUTHOR]

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi‐strategy self‐optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full‐rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition‐based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self‐learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine‐exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Nowadays, Long‐Term Evolution Advanced (LTE‐A) network is the primary innovation in 4G networks. The LTE‐A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE‐A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0‐1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0‐1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)–based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE‐A network than state‐of‐art methods in the proposed strategy. [ABSTRACT FROM AUTHOR]